Images taken by digital cameras can include noisy pixel values, and the noisy pixel values can be detrimental to visual quality of an image. Such image noise can result from various sources. For example, image noise can result from the manner in which photons arrive at sensors in digital cameras (e.g., Poisson photon noise). As another example, image noise can result from electronic components in digital cameras. In particular, the image sensor and/or electronics in digital cameras can generate noise when they capture and process the received photons. Image noise from these various sources can be further accentuated by the post-processing of captured images. For example, the image noise can be amplified by contrast enhancement techniques or blur removal techniques.
There have been attempts to remove image noise from captured images. The image noise removal process is sometimes referred to as denoising. A simple denoising process includes replacing the noisy pixel value with an average of values in the neighboring pixels. The averaging operation can reduce the standard deviation of the noise power in a pixel value by the square root of the number of pixels included in the averaging process. The denoising process can also be based on a filtering operation. For example, a noisy image can be filtered using a Gaussian filter or a Wiener filter to remove high frequency content from the noisy image.
However, these denoising processes do not necessarily improve the image quality of the denoised image. In the case of the simple average-based denoising process, the key challenge is to find pixels with similar properties in the neighborhood, which is often difficult. In the case of the filtering-based denoising process, while the filtering process can remove high frequency noise from the noisy image, the filtering process can be ineffective in removing low frequency noise. Furthermore, if the underlying noise-free image contains high-frequency contents (e.g., fine details and structures), then the filtering process would remove the high-frequency image contents as well as the noise, thereby reducing the quality of the noise-removed image. Therefore, there is a need to improve the denoising process.